We consider Bayesian persuasion with a single Sender and a single Receiver, as introduced by [1]}. Fix a space of \(k\)  states of nature \(\Omega \)  and a commonly-known prior distribution \({p}\)  on them. Take some compact nonempty set \(A\)  to be Receiver's action space. Introduce two random variables \(\omega \)  and \(x\) , representing the state of nature and Receiver's action, respectively. Fix a Sender's utility function \(\tilde{u}_s:A\times \Omega \rightarrow \mathbb {R}_{\ge 0}\)  and a Receiver's utility function \(u_r:A\times \Omega \rightarrow \mathbb {R}_{\ge 0}\) . The Sender-Receiver communication is specified by a signaling scheme \(\Sigma \) , a.k.a. a signaling policy, which is a randomized function from \(\Omega \)  to some set of signals (this notion will be formalized soon). Sender must commit to \(\Sigma \)  before learning \(\omega \) .
